Four-Sequence Maximum Entropy Discrimination Algorithm for Glioma Grading
Four-Sequence Maximum Entropy Discrimination Algorithm for Glioma Grading
复制标题
胶质瘤分级的四序列最大熵判别算法
DOI：
10.1109/access.2019.2910849
复制
发表时间：
2019
期刊：
影响因子：
3.9
通讯作者：
Wang Meiyun
中科院分区：
文献类型：
--
作者：
Wu Yaping;Hao Huihui;Li Jie;Wu Weiguo;Lin Yusong;Wang Meiyun
胶质瘤的分级对于治疗决策以及预后评估至关重要。在临床常规中，放射科医生用多个互补的磁共振成像（MRI）序列对胶质瘤进行分级，这对于胶质瘤预测模型来说是具有挑战性的。在本文中，我们充分利用四种常用的MRI序列，提出了基于最大熵判别法（MED）和决策树的胶质瘤无创分级。首先，分别对T1加权成像、T2加权成像、流体衰减反转恢复成像和对比度增强T1加权成像执行放射组学特征计算。在此基础上，根据不同序列分类边界一致的假设，综合放射组学特征，建立了四序列MED（FSMED）神经胶质瘤预测模型。最后，我们提出了一个多MED决策树（MMEDT）模型，以获得神经胶质瘤的分级的基础上的输出FSMED和MED的结果对每个序列。对从河南省人民医院（GliomaHPPH 2018）和多模态脑肿瘤图像分割基准2017（BraTS 2017）收集的数据集进行了验证实验。这两个数据集的结果证明了我们的方法的高预测性能。GliomaHPPH 2018、BraTS 2017及其合并集的MMEDT平均曲线下面积（AUC）分别为0.9119、0.8184和0.9084，相应的平均灵敏度分别为92.55%、87.85%和87.91%，平均特异性分别为92.57%、81.36%和87.39%。
Grading of glioma is crucial for treatment decision making as well as prognostic assessments. In clinical routines, radiologists grade gliomas with multiple complementary magnetic resonance imaging (MRI) sequences, which is yet challenging for glioma prediction models. In this paper, we take full advantages of four commonly used MRI sequences to propose non-invasive grading of glioma based on a variant of maximum entropy discrimination (MED) and decision tree. First, radiomics features calculation is, respectively, performed on T1-weighted imaging, T2-weighted imaging, fluid attenuation inversion recovery imaging, and contrast-enhanced T1-weighted imaging. Then, radiomics features are integrated to build a glioma prediction model named four-sequence MED (FSMED) according to the assumption that the classification margin of different sequences is consistent. Finally, we propose a multi-MED decision tree (MMEDT) model to obtain the grading of gliomas based on the output of FSMED and the results of MED on each sequence. Validation experiments are conducted on a data set collected from Henan Provincial People’s Hospital (GliomaHPPH2018) and Multimodal Brain Tumor Image Segmentation Benchmark 2017 (BraTS2017). The results of these two data sets demonstrate the high-prediction performance of our method. The average areas under the curve (AUC) of MMEDT are 0.9119, 0.8184, and 0.9084 for GliomaHPPH2018, BraTS2017, and their merged set, respectively, with the corresponding average sensitivities of 92.55%, 87.85%, and 87.91%, and average specificities of 92.57%, 81.36%, and 87.39%.